Elabd Abdelrahman, Razavimaleki Vesal, Huang Shi-Yu, Duarte Javier, Atkinson Markus, DeZoort Gage, Elmer Peter, Hauck Scott, Hu Jin-Xuan, Hsu Shih-Chieh, Lai Bo-Cheng, Neubauer Mark, Ojalvo Isobel, Thais Savannah, Trahms Matthew
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, United States.
Department of Physics, University of California, San Diego, La Jolla, CA, United States.
Front Big Data. 2022 Mar 23;5:828666. doi: 10.3389/fdata.2022.828666. eCollection 2022.
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph-nodes represent hits, while edges represent possible track segments-and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.
在欧洲核子研究组织大型强子对撞机（LHC）的碰撞中确定带电粒子轨迹是一个重要但具有挑战性的问题，特别是在LHC未来高亮度阶段（HL-LHC）预期的高相互作用密度条件下。图神经网络（GNN）是一种几何深度学习算法，通过将追踪器数据作为图进行嵌入（节点表示击中，边表示可能的轨迹段）并将边分类为真实或虚假轨迹段，已成功应用于这项任务。然而，由于其巨大的计算成本，它们在基于硬件或软件的触发应用中的研究受到限制。在本文中，我们引入了一种自动翻译工作流程，该流程集成到一个名为hls4ml的更广泛工具中，用于将GNN转换为现场可编程门阵列（FPGA）的固件。我们使用这个翻译工具在针对不同图大小、任务复杂度以及延迟/吞吐量要求进行设计的FPGA上，实现用于带电粒子追踪的GNN，该GNN使用TrackML挑战数据集进行训练。这项工作可以使HL-LHC实验在触发级别纳入带电粒子追踪GNN。
